Image blur can be introduced in a number of ways when a camera is used to capture an image. Image blur can have various causes, such as movement of the camera or the subject being photographed, incorrect focus, or inherent features of the camera, such the camera's pixel size, the resolution of the camera's sensor, or its use of anti-aliasing filters on the sensor.
FIG. 1 shows a blurry image 100 and a de-blurred image 102. Image de-blurring has been performed previously in a number of ways. For example, an image can be de-blurred using a deconvolution algorithm, which previously has hinged on accurate knowledge of a blur kernel (blur kernels are described in greater detail further below). Thus, finding a blur kernel is an important and useful endeavor. Blur kernels can have uses other than deconvolution. For example, in applications where blur is desirable, such as determining pixel depth from a camera's de-focus, it may also be helpful to recover the shape and size of a spatially varying blur kernel.
However, recovering a blur kernel from a single blurred image is an inherently difficult problem due to the loss of information during blurring. The observed blurred image provides only a partial constraint on the solution, as there are many combinations of blur kernels and sharp images that can be convolved to match the observed blurred image.
Techniques related to finding a blur kernel of an image are discussed below.